Range probing as a quality control tool for CBCT-based synthetic CTs: In vivo application for head and neck cancer patients

Abstract

Purpose: Cone-beam CT (CBCT)-based synthetic CTs (sCT) produced with a deep convolutional neural network (DCNN) show high image quality, suggesting their potential usability in adaptive proton therapy workflows. However, the nature of such workflows involving DCNNs prevents the user from having direct control over their output. Therefore, quality control (QC) tools that monitor the sCTs and detect failures or outliers in the generated images are needed. This work evaluates the potential of using a range-probing (RP)-based QC tool to verify sCTs generated by a DCNN. Such a RP QC tool experimentally assesses the CT number accuracy in sCTs.

Methods: A RP QC dataset consisting of repeat CTs (rCT), CBCTs, and RP acquisitions of seven head and neck cancer patients was retrospectively assessed. CBCT-based sCTs were generated using a DCNN. The CT number accuracy in the sCTs was evaluated by computing relative range errors between measured RP fields and RP field simulations based on rCT and sCT images.

Results: Mean relative range errors showed agreement between measured and simulated RP fields, ranging from -1.2% to 1.5% in rCTs, and from -0.7% to 2.7% in sCTs.

Conclusions: The agreement between measured and simulated RP fields suggests the suitability of sCTs for proton dose calculations. This outcome brings sCTs generated by DCNNs closer toward clinical implementation within adaptive proton therapy treatment workflows. The proposed RP QC tool allows for CT number accuracy assessment in sCTs and can provide means of in vivo range verification.

Keywords: adaptive proton therapy; neural networks; proton radiography; quality control; synthetic CT.

FIGURE 1

Coronal and sagittal views of an example patient geometry (patient 3). The treatment isocenter is shown in yellow and the edges of the RP field are highlighted in orange. In the coronal view, the beam direction is marked by the arrow and the MLIC would be located at the left side of the patient (not depicted). In the sagittal view, the proton spots are directed from behind the patient toward the observer [Color figure can be viewed at wileyonlinelibrary.com]

FIGURE 2

Setup for RP acquisition. The gantry is set to an angle of 90 degrees, directing proton beams from right to left through a patient (not depicted) laying on the table. The MLIC is positioned on a trolley [Color figure can be viewed at wileyonlinelibrary.com]

FIGURE 3

Fusion of a rCT (magenta) with the corresponding sCT (green) in an example patient (patient 1). The treatment isocenter is marked in yellow and the RP field edges are marked in orange. (a): Coronal view of the patient, in which the beam direction is indicated by an orange arrow. (b): Sagittal view of the patient in which a region referred as “A” is highlighted by a blue circle. Region A encloses an exemplary area in the throat that is anatomically unstable [Color figure can be viewed at wileyonlinelibrary.com]

FIGURE 4

RRE maps obtained for patient 1 in session 1 overlaid with a sagittal view of the corresponding CT where RP simulations were performed. Left and right side maps correspond to RP simulations performed in rCT and sCT, respectively. RREs corresponding to proton spots close to the shoulders or in anatomically unstable regions are shown in black and white, respectively. RREs included in the post‐processed dataset are shown in yellow. The edges of the RP field are highlighted in orange [Color figure can be viewed at wileyonlinelibrary.com]

FIGURE 5

Mean RREs and 1.5SD (error bars) for each patient using the post‐processed dataset. Mean RREs are displayed as a result of comparing RP measurements and RP simulations based on rCT (blue color) or sCT (red color). The quantification is reported for both measurement sessions (session 1 and session 2) [Color figure can be viewed at wileyonlinelibrary.com]